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Exploiting Web Sites Structural and Content Features for Web Pages Clustering

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Foundations of Intelligent Systems (ISMIS 2017)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10352))

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Abstract

Web page clustering is a focal task in Web Mining to organize the content of websites, understanding their structure and discovering interactions among web pages. It is a tricky task since web pages have multiple dimension based on textual, hyperlink and HTML formatting (i.e. HTML tags and visual) properties. Existing algorithms use this information almost independently, mainly because it is difficult to combine them. This paper makes a contribution on clustering of web pages in a website by taking into account a distributional representation that combines all these features into a single vector space. The approach first crawls the website by using web pages’ HTML formatting and web lists in order to identify and represent the hyperlink structure by means of an adapted skip-gram model. Then, this hyperlink structure and the textual information are fused into a single vector space representation. The obtained representation is used to cluster websites using simultaneously their hyperlink structure and textual information. Experiments on real websites show that the proposed method improves clustering results.

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References

  1. Angelova, R., Siersdorfer, S.: A neighborhood-based approach for clustering of linked document collections. In: Proceedings of CIKM 2006, pp. 778–779. ACM, New York (2006)

    Google Scholar 

  2. Bohunsky, P., Gatterbauer, W.: Visual structure-based web page clustering and retrieval. In: Proceedings of the 19th International Conference on World Wide Web, WWW 2010, pp. 1067–1068. ACM, New York (2010)

    Google Scholar 

  3. Buttler, D.: A short survey of document structure similarity algorithms. In: Proceedings of the International Conference on Internet Computing, IC 2004, Las Vegas, Nevada, USA, 21–24 June 2004, vol. 1, pp. 3–9 (2004)

    Google Scholar 

  4. Calado, P., Cristo, M., Moura, E., Ziviani, N., Ribeiro-Neto, B., Gonçalves, M.A.: Combining link-based and content-based methods for web document classification. In: Proceedings of the Twelfth International Conference on Information and Knowledge Management, CIKM 2003, pp. 394–401. ACM, New York (2003)

    Google Scholar 

  5. Campello, R.J.G.B., Moulavi, D., Sander, J.: Density-based clustering based on hierarchical density estimates. In: Pei, J., Tseng, V.S., Cao, L., Motoda, H., Xu, G. (eds.) PAKDD 2013. LNCS, vol. 7819, pp. 160–172. Springer, Heidelberg (2013). doi:10.1007/978-3-642-37456-2_14

    Chapter  Google Scholar 

  6. Chehreghani, M.H., Abolhassani, H., Chehreghani, M.H.: Improving density-based methods for hierarchical clustering of web pages. Data Knowl. Eng. 67(1), 30–50 (2008)

    Article  Google Scholar 

  7. Crescenzi, V., Merialdo, P., Missier, P.: Clustering web pages based on their structure. Data Knowl. Eng. 54(3), 279–299 (2005)

    Article  Google Scholar 

  8. Fathi, M., Adly, N., Nagi, M.: Web documents classification using text, anchor, title and metadata information. In: Proceedings of the International Conference on Computer Science, Software Engineering, Information Technology, e-Business and Applications, pp. 1–8 (2004)

    Google Scholar 

  9. Firth, J.: A synopsis of linguistic theory 1930-55. In: Palmer, F.R. (ed.) Selected Papers of J.R. Firth 1952-59, pp. 168–205. Longmans, London (1968)

    Google Scholar 

  10. Fumarola, F., Weninger, T., Barber, R., Malerba, D., Han, J.: Hylien: a hybrid approach to general list extraction on the web. In: Proceedings of the 20th International Conference on World Wide Web, WWW 2011, Hyderabad, India, 28 March - 1 April 2011 (Companion Volume), pp. 35–36 (2011)

    Google Scholar 

  11. Gornerup, O., Gillblad, D., Vasiloudis, T.: Knowing an object by the company it keeps: a domain-agnostic scheme for similarity discovery. In: Proceedings of the 2015 IEEE International Conference on Data Mining (ICDM), ICDM 2015, pp. 121–130. IEEE Computer Society, Washington, DC (2015)

    Google Scholar 

  12. Halko, N., Martinsson, P.G., Tropp, J.A.: Finding structure with randomness: probabilistic algorithms for constructing approximate matrix decompositions. SIAM Rev. 53(2), 217–288 (2011)

    Article  MathSciNet  MATH  Google Scholar 

  13. Haveliwala, T.H., Gionis, A., Klein, D., Indyk, P.: Evaluating strategies for similarity search on the web. In: Proceedings of WWW 2002, pp. 432–442. ACM, New York (2002)

    Google Scholar 

  14. Hubert, L., Arabie, P.: Comparing partitions. J. Classif. 2(1), 193–218 (1985)

    Article  MATH  Google Scholar 

  15. Lanotte, P.F., Fumarola, F., Ceci, M., Scarpino, A., Torelli, M.D., Malerba, D.: Automatic extraction of logical web lists. In: Andreasen, T., Christiansen, H., Cubero, J.-C., Raś, Z.W. (eds.) ISMIS 2014. LNCS, vol. 8502, pp. 365–374. Springer, Cham (2014). doi:10.1007/978-3-319-08326-1_37

    Google Scholar 

  16. Lin, C.X., Yu, Y., Han, J., Liu, B.: Hierarchical web-page clustering via in-page and cross-page link structures. In: Zaki, M.J., Yu, J.X., Ravindran, B., Pudi, V. (eds.) PAKDD 2010. LNCS (LNAI), vol. 6119, pp. 222–229. Springer, Heidelberg (2010). doi:10.1007/978-3-642-13672-6_22

    Chapter  Google Scholar 

  17. Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. Adv. Neural Inf. Process. Syst. 26, 3111–3119 (2013)

    Google Scholar 

  18. Perozzi, B., Al-Rfou, R., Skiena, S.: Deepwalk: online learning of social representations. In: ACM SIGKDD 2014, KDD 2014, pp. 701–710. ACM, New York (2014)

    Google Scholar 

  19. Qi, X., Davison, B.D.: Knowing a web page by the company it keeps. In: Proceedings of the 15th ACM International Conference on Information and Knowledge Management, CIKM 2006, pp. 228–237. ACM, New York (2006)

    Google Scholar 

  20. Rosenberg, A., Hirschberg, J.: V-measure: a conditional entropy-based external cluster evaluation measure. In: EMNLP-CoNLL 2007, pp. 410–420 (2007)

    Google Scholar 

  21. Sahlgren, M.: The distributional hypothesis. Ital. J. Linguist. 20(1), 33–54 (2008)

    Google Scholar 

  22. Tang, J., Qu, M., Wang, M., Zhang, M., Yan, J., Mei, Q.: Line: large-scale information network embedding. In: Proceedings of the 24th International Conference on World Wide Web, WWW 2015, New York, NY, USA, pp. 1067–1077 (2015)

    Google Scholar 

  23. Turney, P.D., Pantel, P.: From frequency to meaning: vector space models of semantics. J. Artif. Int. Res. 37(1), 141–188 (2010)

    MathSciNet  MATH  Google Scholar 

  24. Weninger, T., Johnston, T.J., Han, J.: The parallel path framework for entity discovery on the web. ACM Trans. Web 7(3), 16:1–16:29 (2013)

    Google Scholar 

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Acknowledgments

We acknowledge the support of the EU Commission through the project MAESTRA - Learning from Massive, Incompletely annotated, and Structured Data (Grant number ICT-2013-612944).

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Authors and Affiliations

  1. University of Bari Aldo Moro, via Orabona 4, 70125, Bari, Italy

    Pasqua Fabiana Lanotte, Donato Malerba & Michelangelo Ceci

  2. Unicredit Research and Development, 20100, Milan, Italy

    Fabio Fumarola

  3. CINI - Consorzio Interuniversitario Nazionale per l’Informatica, Bari, Italy

    Donato Malerba & Michelangelo Ceci

Authors
  1. Pasqua Fabiana Lanotte
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  2. Fabio Fumarola
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  3. Donato Malerba
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  4. Michelangelo Ceci
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Corresponding author

Correspondence to Pasqua Fabiana Lanotte .

Editor information

Editors and Affiliations

  1. Warsaw University of Technology, Warsaw, Poland

    Marzena Kryszkiewicz

  2. University of Bari Aldo Moro, Bari, Italy

    Annalisa Appice

  3. Institute of Informatics, University of Warsaw, Warsaw, Poland

    Dominik Ślęzak

  4. Faculty of Electronics & Information, Warsaw University of Technology, Warsaw, Poland

    Henryk Rybinski

  5. Institute of Mathematics, Warsaw University, Warsaw, Poland

    Andrzej Skowron

  6. Department of Computer Science, University of North Carolina at Charlotte, North Carolina, USA

    Zbigniew W. Raś

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Lanotte, P.F., Fumarola, F., Malerba, D., Ceci, M. (2017). Exploiting Web Sites Structural and Content Features for Web Pages Clustering. In: Kryszkiewicz, M., Appice, A., Ślęzak, D., Rybinski, H., Skowron, A., Raś, Z. (eds) Foundations of Intelligent Systems. ISMIS 2017. Lecture Notes in Computer Science(), vol 10352. Springer, Cham. https://doi.org/10.1007/978-3-319-60438-1_44

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  • DOI: https://doi.org/10.1007/978-3-319-60438-1_44

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-60437-4

  • Online ISBN: 978-3-319-60438-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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